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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： P. Tapan Datta D.Lalith Kumar S. Tanoj Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In this study , the reasearch is focused on the simulation of vehicular communication networks, especially intelligent traffic systems, to enhance dependability, safety, and optimality in communication. The simulation is performed using the OMNeT++ simulator and is integrated with the Veins framework that uses the IEEE 802.11p protocol for V2V and V2I communications. Importing real-world traffic scenarios into the framework is obtained via the use of SUMO through TraCIScenarioManager. Other features of the framework include the definition of mobility models, dynamic obstacles, and detailed radio propagation modeling. TraCIDemo11p is used for mobility and communication by both RSUs and vehicles with applications in beaconing and channel switching. The whole framework analyzes smart traffic management solutions, providing as much insight into their performance based upon the communication reliability and overall efficiency in vehicular networks.

关键词： Protocols Vehicle-to-infrastructure Simulation Vehicular ad hoc networks Switches Radio propagation Safety Security Reliability Vehicle dynamics

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Enhancing Water Quality Prediction with Machine Learning and Explainable AI Techniques

Enhancing Water Quality Prediction with Machine Learning and...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： D. Sai Nikhil Reddy G. Mahendra Babu G. Uttej C. Jyotsna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Different pollutants have affected the quality of water in the recent past in a big way. Clean drinking water is important for every living creature on this planet because the second important natural resource, water, can be used in many ways for instance in agriculture, to water livestock, or drink. Proposed methodology ensures water quality by applying various computational algorithms like Decision Trees, K-Nearest Neighbor, RandomForest, XGBoost, CatBoost, Logistic Regression, stacking classifier. Synthetic Minority Over-sampling Technique has used to increase the size of minority class and avoid an over fitted model Light-gbm has given the best accuracy of 96 %. The different methods used in this work include Explainable AI (XAI) methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) are used to make the results understandable to user. This helps in identifying the key features responsible for determining the water quality such as pollutant levels, mineral content. The proposed model provides a user friendly graphical user interface which allows the user to access and interpret the results and indicates weather the water is safe for consumption or not.

关键词： Accuracy Additives Explainable AI Biological system modeling Stacking Water quality Water pollution Safety Sustainable development Water resources

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Li-Fi Enabled Gas Detection: A Novel Approach for Navy Ship Safety

Li-Fi Enabled Gas Detection: A Novel Approach for Navy Ship ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Ashwanth G P N Gajjala Venkat Rushith Reddy Prayaga Shashank Abhiram Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The major risk of gas leakages to naval ships and vessels is to both personnel and machinery. Traditional types of gas detectors use fixed wired sensors, which tend to be extremely challenging for maintenance in complex ship contexts. This work addresses applying Li-Fi technology for successful wireless detection of gas leaks. Li-Fi has been employed to measure the gas level in real time, hence enabling fast reaction in case of possible leaks or damage. The foundation of this project is to establish a LiFi-driven communication system in areas where Radio Frequency(RF) communication is restricted and provides safer and more reliable detection system for navy ships. Preliminary results show that in this Li-Fi-based system, accurate real-time gas detection with high speed data transfer in RF challenged environments should be possible. This type of approach not only will increase response times but the need for vast wiring diminishes, hence enhancing effectiveness in maintenance and operational dependability in complex ship spaces.

关键词： Wiring Wireless communication Wireless sensor networks Light fidelity Real-time systems Safety Maintenance Velocity measurement Marine vehicles Gas detectors

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Assessment of Online Teaching Using Statistical and Unsupervised Learning Methods

Assessment of Online Teaching Using Statistical and Unsuperv...

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International Conference on Power engineering and Intelligent Systems, PEIS 2023

作者： Bisht, Raj Kishor Jasola, Sanjay Bisht, Ila Pant Lohumi, Yogesh School of Computing Graphic Era Hill University Uttarakhand Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Department of Economics and Statistics Government of Uttarakhand Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9789819972159

The objective of the present study is to assess different aspects of online teaching from faculties’ perspective. We investigate the level of the difficulties faced by faculty members in handling Moodle, the satisfaction level of online teaching, and online evaluation, and the role of previous awareness/training programs in facing difficulties handling Moodle. Finally, we find the grey areas in online teaching. We collected data from 104 faculty members of Graphic Era Hill University (GEHU) for the present study. Analysis of variance approach and t-test are used for different purposes. K-Means Clustering method is used for analyzing the role of training programs in facing difficulties in handling Moodle. We found that different aspects of Moodle handling were considered equally difficult;satisfaction level in content delivery only is considered high. Satisfaction levels in two components of evaluation, multiple choice questions (MCQ) and descriptive questions (DQ) were neither considered the same nor high;however, satisfaction in MCQ is considered a little more than in DQ. Through clustering patterns, we found a significant effect of previous awareness/training in facing difficulty in handling Moodle. These findings may be useful for making strategies for online teaching in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Facings

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A Squeeze-Excitation ResNet Approach for Effective Classification of Parasitic Eggs

A Squeeze-Excitation ResNet Approach for Effective Classific...

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2024 International Visualization, Informatics and Technology Conference, IVIT 2024

作者： Muthulakshm, M. Venkatesan, K. Rahayu, Syarifah Bahiyah Elangovan, Karthickeien Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India National Defence University Malaysia Faculty of Defense Science and Technology Kuala Lumpur Malaysia National Defence University Malaysia Cyber Security and Digital Industrial Revolution Centre Kuala Lumpur Malaysia

ISBN: (纸本)9798350354447

The identification and classification of different kinds of parasite eggs in microscopic samples represent a critical challenge in the field of Soil-transmitted helminth infection diagnosis. Traditional methods are often labor-intensive and time-consuming. The emergence of deep learning models has shown promising results in automating this process by extracting intricate features from complex images. This study aims to develop an automated system for accurately classifying parasite egg types in microscopic images by leveraging the ability of squeeze excitation layers to learn the global information from the input. The proposed system employs features extracted by ResNet50 and ResNet101 with Squeeze Excitation (SE) layers for analysis. The extracted features are then input into a Support Vector Classifier. The study systematically evaluates the features extracted from ResNet50+SE and ResNet101+SE. Results from the evaluation demonstrate the efficacy of the ResNet50+SE in accurately classifying parasite egg types in microscopic images with an accuracy of 0.94. The study provides valuable insights into the choice of squeeze-excitation block added Resnet in the context of parasitology, contributing to the advancement of automated medical image analysis. The findings hold great potential for improving diagnostic processes and supporting epidemiological studies through efficient and accurate parasite detection. © 2024 IEEE.

关键词： Deep learning

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Pneumonia Disease Assessment with Full Diagnostics Evaluation

Pneumonia Disease Assessment with Full Diagnostics Evaluatio...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Rayala Nithin G Nagarajan Thammishetti Venkat Sai Prathap Srikonda Pradeep Itharaju Sampreeth Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Virudhnagar

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Pneumonia remains a critical global health issue, causing significant illness and mortality, especially in vulnerable populations. Early and precise diagnosis is essential for effective management and timely the intervention. This research paper introduces a novel integrated approach for the prediction and comprehensive diagnostics of pneumonia, harnessing cutting-edge advancements in medical imaging, machine learning, and clinical data analysis. The proposed framework merges three fundamental components: (1) Chest X-ray Image Analysis, (2) Clinical Data Integration, and (3) Machine Learning-Based Prediction. The chest X-ray image analysis employs state-of-the-art deep learning techniques to automatically identify and classify pneumonia-related anomalies, offering detailed insights into pulmonary conditions. Simultaneously, clinical data, encompassing patient history, demographics, and laboratory results, are integrated into the predictive model. A hybrid machine learning algorithm, combining Convolutional Neural Networks (CNN) and Gradient Boosting, is utilized to ensure robust and precise disease prediction. The research demonstrates the efficacy of this integrated approach through a thorough evaluation on a substantial dataset of pneumonia cases. Findings indicate a notable enhancement in pneumonia prediction accuracy compared to conventional diagnostic methods. Moreover, the model’s interpretability is enhanced, allowing healthcare professionals to scrutinize the rationale behind each prediction, promoting trust and transparency in the decision-making process.

关键词： Support vector machines Pneumonia Image analysis Neural networks Medical services Predictive models Vectors

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Performance Assessment of Multi-Controller in Software Defined Network 4

Performance Assessment of Multi-Controller in Software Defin...

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4th IEEE International Conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Jaiswal, Ankita Patra, Sudhansu Shekhar Dash, Bibhuti Bhusan Barik, Lalbihari Pandey, Trilok Nath Mishra, Manoj Ranjan School of Computer Engineering KIlT Deemed to Be University Bhubaneswar India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350306118

In a network design, the control plane and the data plane are separated by the architectural concept of software defined networking (SDN). A centralised controller that serves as the only point of control for the whole network is introduced by the SDN architecture. The controller serves as a logically centralized entity in charge of communicating the appropriate rules to a switch's flow table. Using a single controller to handle a lot of flows becomes insufficient. A lot of large-scale networks have employed multi-controller SDN to meet their objectives. In this research, we use a multi-controller infinite buffer waiting queue model to analyze the system performance metrics. Both numerical and simulated assessments are used to illustrate the proposed model. © 2023 IEEE.

关键词： Controllers

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Exploring the Power of Entangled Data in Quantum Machine Learning

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Wuhan University Journal of Natural sciences 2024年第3期29卷 193-194页

作者： WANG Xinbiao DU Yuxuan TU Zhuozhuo LUO Yong YUAN Xiao TAO Dacheng Institute of Artificial Intelligence School of Computer ScienceWuhan UniversityWuhan 430072HubeiChina National Engineering Research Center for Multimedia Software Wuhan UniversityWuhan 430072HubeiChina JD Explore Academy Beijing 101111China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Computer Science Faculty of EngineeringUniversity of SydneyNSW 2008Australia Center on Frontiers of Computing Studies Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China

Quantum entanglement is a key resource for achieving superiority of quantum ***,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine learning(QML)models,aiming to surpass the performance of traditional machine learning *** successes include the use of entangled measurements^([1-3])and entangled channels^([4]),which have been shown to reduce query complexity or improve the prediction precision for specified QML *** entangled data,capable of encoding more information compared to classical data of the same size,is recognized for its potential to achieve quantum ***,the impact of the entanglement degree in quantum data on model performance remains a challenging and unresolved research question.

关键词： quantum specified integrate

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Remote Sensing Data-Based Satellite Image Analysis in Water Quality Detection for Public Health Data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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Intelligent Cutter State Detection Platform based on Edge-Enhanced FCN 5

Intelligent Cutter State Detection Platform based on Edge-En...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Wang, Pan Zheng, Liaomo Lian, Mengjia School of Information Science and Engineering Shenyang Ligong University Shenyang110159 China Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang110168 China School of Mathematics and Information Engineering Longyan University Longyan361000 China

ISBN: (纸本)9798350357950

In the manufacturing industry, machining is a crucial process, and tool wear is often a significant concern. During high-speed machining, tool breakage and excessive wear can lead to workpiece rejection and even damage to the tool holder and machine tool. Therefore, in-depth research on the detection, assessment, and prevention of tool wear is of paramount importance to the manufacturing industry. Tool wear manifests in various forms, including chipping, wear on the rake face, built-up edge, and edge wear. By developing efficient tool wear detection techniques, manufacturers can rapidly and accurately assess the quality and performance of tools from different batches and manufacturers, thereby enhancing product quality and production efficiency. Detection methods based on image segmentation can intuitively and efficiently batch detect tool conditions, providing operators with a visual interface to reduce their workload, improve production efficiency and safety, and lower production costs. © 2023 IEEE.

关键词： Edge Attention Mechanism Image Segmentation Tool Wear Detection

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